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This paper aims to identify alternatives for citizens to get out of the bubble when interacting with the information provided by the internet through search engines. This situation arises because there is extensive ignorance about the way search engines operate and the way advanced search algorithms operate, both situations based on the specific segmentation of the public, which creates difficulties in obtaining vast information that allows the contrast and development of diverse perspectives, which do not necessarily lead to the use of critical thinking.

To achieve this contribution, a documentary-type investigation was carried out based on the review of the scientific literature on the subject, and the nature of this investigation, due to its purpose, being descriptive. The study takes as document selection criteria those original articles whose content relevance is linked to the purposes of this research proposal, published in scientific databases (SciELO, RedAlyC, Dialnet, ScienceDirect, WoS and Scopus) mostly between the years 2018 and 2023, in Spanish and English, that described the impact of bubble filters on full access to information and data privacy, for which the topics were addressed: internet search algorithms, bubble filters, search engines, privacy policies in search engines and management of personal data.

At the beginning, this paper clarifies the concepts of search algorithms on the Internet, bubble filters, search engines and privacy policies in search engines, which made it possible to identify alternatives that would allow the user to face the silent reality of the algorithms and avoid the bias in the information that the algorithm provides, in addition, the need to generate algorithmic literacy mechanisms, training in the use of metasearch engines and education in algorithms is proposed, with which citizens can exercise critical thinking in the way they interact on the internet.

This is a theoretical proposal, in which various inferences are made from theoretical knowledge without fieldwork.

It is considered a document of important value in the training processes of librarians and information professionals in the training of users and in the ways of interaction with technology.

This compact case study was developed from secondary sources readily available in the public domain. These secondary sources included websites, videos and articles.

Throughout 2023, social media companies faced a wide range of criticism on several fronts. Critics claimed that the companies were not doing enough to manage content and the algorithms were influencing American public opinion in the Israel-–Hamas war. Others argued that social media was negatively impacting the mental health of American youth. In response, the platforms reiterated their neutrality and emphasized the features, functions and policies that were designed to address the issues and encourage a positive user experience. As generative artificial intelligence (AI) grew in popularity, the impact on social media was inevitable. Was the convergence of social media and AI inspiring progress or exacerbating problems? How would society balance the opposing forces in a rapidly evolving environment?

This case should be used in marketing and management classes at the undergraduate level. Applicable concepts include AI, social media, content and information.

In response to growing concerns over the negative consequences of Internet addiction on adolescents’ mental health, coupled with conflicting results in this literature stream, this meta-analysis sought to (1) examine the association between Internet addiction and depressive symptoms in adolescents, (2) examine the moderating role of Internet freedom across countries, and (3) examine the mediating role of excessive daytime sleepiness.

In total, 52 studies were analyzed using robust variance estimation and meta-analytic structural equation modeling.

There was a significant and moderate association between Internet addiction and depressive symptoms. Furthermore, Internet freedom did not explain heterogeneity in this literature stream before and after controlling for study quality and the percentage of female participants. In support of the displacement hypothesis, this study found that Internet addiction contributes to depressive symptoms through excessive daytime sleepiness (proportion mediated = 17.48%). As the evidence suggests, excessive daytime sleepiness displaces a host of activities beneficial for maintaining mental health. The results were subjected to a battery of robustness checks and the conclusions remain unchanged.

The results underscore the negative consequences of Internet addiction in adolescents. Addressing this issue would involve interventions that promote sleep hygiene and greater offline engagement with peers to alleviate depressive symptoms.

This study utilizes robust meta-analytic techniques to provide the most comprehensive examination of the association between Internet addiction and depressive symptoms in adolescents. The implications intersect with the shared interests of social scientists, health practitioners, and policy makers.

The growth of social media globally has led to brands adopting memes as a tool for communication. They have influenced digital culture, politics and marketing. This study aims to highlight the research gap in meme literature by profiling and science mapping.

This study has performed a bibliometric analysis of research papers on memes from the Scopus database. The authors profiled the literature and found the most relevant journal, author and document. Science mapping techniques such as thematic analysis, thematic evolution and co-citation of papers with content analysis were used.

Profiling of literature revealed that the most relevant journal, based on a number of papers and citations, is New Media and Society. The top researcher on memes is Shiffman L (seven papers), and the most cited author is Menczer F (717 citations). The paper titled “Towards A Unified Science of Cultural Evolution” has the highest number of citations (402). Thematic mapping revealed “Social Movements” and “Twitter” are the currently trending themes. The co-citation analysis clustered the literature into two. The first cluster elucidates the theoretical underpinnings of memes, while the second cluster explicates the various contexts in which memes are studied.

This study will be vital to researchers hoping to study internet memes by providing directions for future research. Furthermore, the insights from this study will enable social media managers to use memes effectively.

To the best of the authors’ knowledge, this study seems to be the first to use bibliometric analysis to profile and map meme literature. This study provides a new perspective for meme researchers to explore memes and suggests marketers implement novel meme-based strategies in their marketing communications.

This paper according to the logic of the “digital access divide--digital capability divide--digital outcome divide” aims to systematically discuss the impact of the digital divide on individual happiness in China, accounting for the variations that exist across different groups, as well as the corresponding mechanisms.

This paper presents cross-sectional analyses of the relationship between the digital divide and individual happiness in China. The analyses are based on data from the Chinese General Social Survey 2017, which academic institutions run on the Chinese Mainland. This database contains information on respondents' Internet access, skills and consequences of use, which can measure the digital divide of Chinese individuals at three levels.

First, individual happiness declined when they experienced the digital access divide in China. For the digital capability divide, the lower the usage skills, the more individual happiness declined. When analyzing the digital outcome divide, the greater the negative consequences, the more individual happiness declined. Second, the impacts of digital access, capability and outcome divide vary according to age, gender, education degrees, hukou, region and sub-dimensions. Third, the digital access and capability divide reduce individuals' happiness by lowering their self-rated social and economic status, whereas the digital outcome divide reduce individual happiness by lowering their fairness perception and social trust.

The authors believe that this is the first study to examine the impact and its variations among different groups of the three-level digital divide on individual happiness, as well as its mechanisms.

This study aims to analyze and synthesize literature on consumer privacy-related behavior and intelligent device-to-device interactions within the Internet of Things (IoT).

We conducted a systematic review using Elsevier’s Scopus database, focusing on studies published in English from 2000 to 2023. The review targeted articles within selected social sciences and business disciplines, specifically concerning consumer behavior in IoT contexts.

We categorized the privacy literature into three thematic clusters: legislation and policy, business implications and consumer behavior. Within the consumer behavior cluster, our analysis indicates a shift from general Internet and e-commerce privacy concerns prior to 2016, toward issues related to advertising and policy between 2017 and 2018, and increasingly toward pronounced concerns in technological systems, particularly IoT, from 2019 onwards. We identify eight distinct areas of privacy concern within IoT and propose a framework that links antecedents and privacy concerns to subsequent attitudes and behaviors. This framework highlights varying patterns of information disclosure and bridges theoretical constructs with empirical research in IoT privacy.

Originality lies in enhancing the Antecedents-Privacy Concerns-Outcomes (APCO) macro-model by integrating diverse theoretical perspectives on technological and individual-specific antecedents, alongside privacy concerns and beliefs. This comprehensive integration enriches the framework, enabling it to predict and categorize consumer behavior in IoT environments more effectively. The revised model provides a robust tool for understanding privacy-related behavior within the IoT, significantly enriching its theoretical relevance and practical applicability.

The basic unit of the family is the married couple who are brought together through various means, the internet being an additional medium. This study is an attempt to capture changes in the realm of family through a detailed analysis of the matrimonial websites in India from a sociological perspective. With the advent of the internet technology, it explores whether and how the internet as a new medium of spouse selection influences marriage choices and family dynamics during match-fixing. It seeks to unweave the individual–family–community dynamics in the context of changing gender norms. The findings of the study are based on mixed-methods through the use of both qualitative and quantitative data gathered from the website and personal interviews. These data were further strengthened through the walkthrough method and participant observation. The findings of this study reflect upon the prescriptive, restrictive and supportive role played by the family in the context of online spouse selection. It also documents resilience and change (in terms of family structure and norms) in the realm of the family in the context of changing matrimonial preferences.

The paper addresses the privacy–personalisation paradox in the post-GDPR-2018 era. As the regulation came in a bid to regulate the collection and use of personal data, its implications remain underexplored. The research question is: How do consumers perceive the matter of personal data collection for the use of highly targeted and personalised ads post-GDPR-2018? The invasion of privacy vs the benefits of highly personalised digital marketing.

To address the research question, this qualitative study conducts semi-structured interviews with 14 individuals, consisting of average users and digital experts.

This paper reports on increasing consumer vulnerability post-GDPR-2018 due to increased awareness of personal data collection yet incessant lack of control, particularly regarding the repercussions of the digital footprint. The privacy paradox remains an issue except among experts, and personalisation remains necessary, yet critical challenges arise (e.g. filter bubbles and intrusion).

Policy implications include education, regulating consent platforms and encouraging consensual sharing of personal data.

While the privacy–personalisation paradox has been widely studied, the impact of GDPR-2018 has rarely been addressed in the literature. GDPR-2018 has seemingly had little impact on instilling a sense of security for consumers; if anything, this paper highlights greater concerns for privacy as users sign away their rights on consent forms to access websites, thus contributing novel insights to this area of research.

The increasing popularity of music streaming services also increases the need to customize the services for each user to attract and retain customers. Most of the music streaming services will not have explicit ratings for songs; they will have only implicit feedback data, i.e user listening history. For efficient music recommendation, the preferences of the users have to be infered, which is a challenging task.

Preferences of the users can be identified from the users' listening history. In this paper, a hybrid music recommendation system is proposed that infers features from user's implicit feedback and uses the hybrid of content-based and collaborative filtering method to recommend songs. A Content Boosted K-Nearest Neighbours (CBKNN) filtering technique was proposed, which used the users' listening history, popularity of songs, song features, and songs of similar interested users for recommending songs. The song features are taken as content features. Song Frequency–Inverse Popularity Frequency (SF-IPF) metric is proposed to find the similarity among the neighbours in collaborative filtering. Million Song Dataset and Echo Nest Taste Profile Subset are used as data sets.

The proposed CBKNN technique with SF-IPF similarity measure to identify similar interest neighbours performs better than other machine learning techniques like linear regression, decision trees, random forest, support vector machines, XGboost and Adaboost. The performance of proposed SF-IPF was tested with other similarity metrics like Pearson and Cosine similarity measures, in which SF-IPF results in better performance.

This method was devised to infer the user preferences from the implicit feedback data and it is converted as rating preferences. The importance of adding content features with collaborative information is analysed in hybrid filtering. A new similarity metric SF-IPF is formulated to identify the similarity between the users in collaborative filtering.

This study aims to provide an excellent overview of current research trends in the construction sector in digital advancements. It provides a roadmap to policymakers for the effective utilisation of emergent digital technologies and a need for a managerial shift for its smooth adoption.

A total of 3,046 peer-reviewed journal review articles covering Internet of Things (IoT), blockchain, building information modelling (BIM) and digital technologies within the construction sector were reviewed using scientometric mapping and weighted mind-map analysis techniques.

Prominent research clusters identified were: practice-factor-strategy, system, sustainability, BIM and construction worker safety. Leading countries, authors, institutions and their collaborative networks were identified with the UK, the USA, China and Australia leading this field of research. A conceptual framework for an IoT-based concrete lifecycle quality control system is provided.

The study traces the origins of the initial application of Industry 4.0 concepts in the construction field and reviews available literature from 1983 to 2021. It raises awareness of the latest developments and potential landscape realignment of the construction industry through digital technologies conceptual framework for an IoT-based concrete lifecycle quality control system is provided.